Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
This work addresses early Alzheimer's disease diagnosis for patients using neuroimaging, presenting an incremental improvement over existing multimodal fusion approaches.
The paper tackles the problem of biased and noisy representations in multimodal neuroimaging fusion for Alzheimer's disease diagnosis by proposing a Collaborative Attention and Consistent-Guided Fusion framework that preserves both shared and specific features while aligning latent distributions. The method achieves superior diagnostic performance on the ADNI dataset compared to existing fusion strategies.
Alzheimer's disease (AD) is the most prevalent form of dementia, and its early diagnosis is essential for slowing disease progression. Recent studies on multimodal neuroimaging fusion using MRI and PET have achieved promising results by integrating multi-scale complementary features. However, most existing approaches primarily emphasize cross-modal complementarity while overlooking the diagnostic importance of modality-specific features. In addition, the inherent distributional differences between modalities often lead to biased and noisy representations, degrading classification performance. To address these challenges, we propose a Collaborative Attention and Consistent-Guided Fusion framework for MRI and PET based AD diagnosis. The proposed model introduces a learnable parameter representation (LPR) block to compensate for missing modality information, followed by a shared encoder and modality-independent encoders to preserve both shared and specific representations. Furthermore, a consistency-guided mechanism is employed to explicitly align the latent distributions across modalities. Experimental results on the ADNI dataset demonstrate that our method achieves superior diagnostic performance compared with existing fusion strategies.